"""simple docstring"""


from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a				=logging.get_logger(__name__)

a				={
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[str]    				=			'''autoformer'''
				A__ :  int    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''num_hidden_layers''': '''encoder_layers''',
				}

				def __init__( self      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = "student_t"      ,					_lowerCamelCase = "nll"      ,					_lowerCamelCase = 1      ,					_lowerCamelCase = [1, 2, 3, 4, 5, 6, 7]      ,					_lowerCamelCase = True      ,					_lowerCamelCase = 0      ,					_lowerCamelCase = 0      ,					_lowerCamelCase = 0      ,					_lowerCamelCase = 0      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = 64      ,					_lowerCamelCase = 2      ,					_lowerCamelCase = 2      ,					_lowerCamelCase = 2      ,					_lowerCamelCase = 2      ,					_lowerCamelCase = 32      ,					_lowerCamelCase = 32      ,					_lowerCamelCase = "gelu"      ,					_lowerCamelCase = 0.1      ,					_lowerCamelCase = 0.1      ,					_lowerCamelCase = 0.1      ,					_lowerCamelCase = 0.1      ,					_lowerCamelCase = 0.1      ,					_lowerCamelCase = 100      ,					_lowerCamelCase = 0.0_2      ,					_lowerCamelCase = True      ,					_lowerCamelCase=True      ,					_lowerCamelCase = 10      ,					_lowerCamelCase = 25      ,					_lowerCamelCase = 3      ,					**_lowerCamelCase      ,					):
										# time series specific configuration
										lowerCamelCase__   =prediction_length
										lowerCamelCase__   =context_length if context_length is not None else prediction_length
										lowerCamelCase__   =distribution_output
										lowerCamelCase__   =loss
										lowerCamelCase__   =input_size
										lowerCamelCase__   =num_time_features
										lowerCamelCase__   =lags_sequence
										lowerCamelCase__   =scaling
										lowerCamelCase__   =num_dynamic_real_features
										lowerCamelCase__   =num_static_real_features
										lowerCamelCase__   =num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
																if len(_lowerCamelCase ) != num_static_categorical_features:
																						raise ValueError(
																						    "The cardinality should be a list of the same length as `num_static_categorical_features`" )
																lowerCamelCase__   =cardinality
										else:
																lowerCamelCase__   =[0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
																if len(_lowerCamelCase ) != num_static_categorical_features:
																						raise ValueError(
																						    "The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
																lowerCamelCase__   =embedding_dimension
										else:
																lowerCamelCase__   =[min(50      ,					(cat + 1) // 2 ) for cat in self.cardinality]
										lowerCamelCase__   =num_parallel_samples

										# Transformer architecture configuration
										lowerCamelCase__   =input_size * len(self.lags_sequence ) + self._number_of_features
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =decoder_layers

										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =decoder_layerdrop

										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std

										lowerCamelCase__   =use_cache

										# Autoformer
										lowerCamelCase__   =label_length
										lowerCamelCase__   =moving_average
										lowerCamelCase__   =autocorrelation_factor

										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )






				@property
				def 						_a      ( self ):
										return (
										    sum(self.embedding_dimension )
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)



"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	a				=None

a				=logging.get_logger(__name__)
a				={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a				={
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

a				={
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

a				='▁'






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Dict    				=			VOCAB_FILES_NAMES
				A__ :  List[Any]    				=			PRETRAINED_VOCAB_FILES_MAP
				A__ :  List[Any]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__ :  Union[str, Any]    				=			AlbertTokenizer

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<unk>"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<pad>"      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[MASK]"      ,					**_lowerCamelCase      ,					):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowerCamelCase__   =(
										    AddedToken(_lowerCamelCase      ,					lstrip=_lowerCamelCase      ,					rstrip=_lowerCamelCase      ,					normalized=_lowerCamelCase )
										    if isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										    else mask_token
										)

										super().__init__(
										    _lowerCamelCase      ,					tokenizer_file=_lowerCamelCase      ,					do_lower_case=_lowerCamelCase      ,					remove_space=_lowerCamelCase      ,					keep_accents=_lowerCamelCase      ,					bos_token=_lowerCamelCase      ,					eos_token=_lowerCamelCase      ,					unk_token=_lowerCamelCase      ,					sep_token=_lowerCamelCase      ,					pad_token=_lowerCamelCase      ,					cls_token=_lowerCamelCase      ,					mask_token=_lowerCamelCase      ,					**_lowerCamelCase      ,					)

										lowerCamelCase__   =do_lower_case
										lowerCamelCase__   =remove_space
										lowerCamelCase__   =keep_accents
										lowerCamelCase__   =vocab_file
										lowerCamelCase__   =False if not self.vocab_file else True

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																    "tokenizer." )

										if not os.path.isdir(_lowerCamelCase ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																return
										lowerCamelCase__   =os.path.join(
										    _lowerCamelCase      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

										if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
																copyfile(self.vocab_file      ,					_lowerCamelCase )

										return (out_vocab_file,)


